Member of Technical Staff — Robotic AI Foundations

Amazon; San Francisco (CA); Presencial; USD 150 000 - 300 000; Tempo integral

Resumo da oferta

Amazon’s Frontier AI & Robotics (FAR) team seeks a Member of Technical Staff to advance foundational research and build intelligent robotic systems at scale. You will pursue first‑principles research, publish, and deploy innovations into production across diverse robotics applications.

You will lead end‑to‑end projects from concept to deployment, collaborating with platform and hardware teams to ensure robust performance in real‑world environments, while advancing large‑scale multi‑modal

Qualificações

• PhD or Master’s degree with 4+ years in CS/CE/ML or related field.
• Experience building ML models or algorithms for real-world applications.
• Publications or patents at top-tier conferences or journals.
• Experience applying deep learning models in production environments.
• Proficiency in Python, Java and C++ for research and deployment.

Responsabilidades

• Drive independent research initiatives across the robotics stack from concept to deployment.
• Design and implement novel deep learning architectures for robotic systems.
• Lead full‑stack robotics projects integrating hardware and algorithms.
• Collaborate with platform and hardware teams to ensure production readiness.
• Mentor researchers while contributing strong personal technical work.
• Publish findings and transfer research into scalable production systems.

Conhecimentos

Python

Java

C++

Deep learning

Publications

ML models

Community papers

Python—PyTorch/JAX

Formação académica

PhD or Master’s degree

Ferramentas

PyTorch/JAX

Descrição da oferta de emprego

Amazon’s Frontier AI & Robotics (FAR) team seeks a Member of Technical Staff to advance foundational research and build intelligent robotic systems at scale. You will pursue first‑principles research, publish, and deploy innovations into production across diverse robotics applications.

You will lead end‑to‑end projects from concept to deployment, collaborating with platform and hardware teams to ensure robust performance in real‑world environments, while advancing large‑scale multi‑modal